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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

arXiv

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arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Univariate Forecasting of time series data Based on Seq2Seq Model 4

Univariate Forecasting of time series data Based on Seq2Seq ...

引用

4th International Conference on Electronic information technology and computer Engineering, EITCE 2020

作者： Guo, Yanwen Wu, Tao Liu, Yanbing Jiang, Yixin Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Internet and Information Sccurity Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781450387811

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction. © 2020 ACM.

关键词： Time series

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Instant Edit Propagation on Images Based on Bilateral Grid

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computers, Materials & Continua 2019年第8期61卷 643-656页

作者： Feng Li Chaofeng Ou Yan Gui Lingyun Xiang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore639798Singapore

The ability to quickly and intuitively edit digital content has become increasingly important in our everyday ***,existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large ***,existing edit propagation methods are generally inefficient and highly ***,to improve edit efficiency,this paper proposes a novel edit propagation method using a bilateral grid,which can achieve instant propagation of sparse image ***,given an input image with user interactions,we resample each of its pixels into a regularly sampled bilateral grid,which facilitates efficient mapping from an image to the bilateral *** a result,all pixels with the same feature information(color,coordinates)are clustered to the same grid,which can achieve the goal of reducing both the amount of image data processing and the cost of *** then reformulate the propagation as a function of the interpolation problem in bilateral space,which is solved very efficiently using radial basis *** results show that our method improves the efficiency of color editing,making it faster than existing edit approaches,and results in excellent edited images with high quality.

关键词： Instant edit propagation bilateral grid radial basis function image editing

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A data clustering strategy for enhancing mutual privacy in healthcare system of IoT

A data clustering strategy for enhancing mutual privacy in h...

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Guo, Xuancheng Lin, Hui Xu, Chuanfeng Lin, Wenzhong School of Mathematics and Computer Science Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9781728152097

Recent advances in the healthcare system of Internet of Things (IoT) has led to a generation of a large amount of physic sensor data. Data analyst collects and analyzes these sensor data through wireless sensor network, so as to provide some treatment advices to physicians and patients. As a common data mining method, the k-means clustering algorithm is being applied to process large-scale sensor data. However, it also poses a threat of privacy leakage in the specific application process. To enhance the privacy in healthcare system of IoT, mutual privacypreserving k-means strategy (M-PPKS) based on homomorphic encryption is proposed in this paper, which neither discloses an individual's private information nor leaks the cluster center's characteristic data. An extension performance evaluation shows that, in the case of ensuring accurate clustering results, even if the analyst and individuals collude, the M-PPKS can prevent the disclosure of private information. © 2019 IEEE.

关键词： Internet of things

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Representation Matching For Remote Quantum Computing

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PRX Quantum 2021年第2期2卷 020327-020327页

作者： Yuxiang Yang Masahito Hayashi Institute for Theoretical Physics ETH Zürich Switzerland QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan

Many quantum computational tasks have inherent symmetries, suggesting a path to enhancing their efficiency and performance. Exploiting this observation, we propose representation matching, a generic probabilistic protocol for reducing the cost of quantum computation in a quantum network. We show that the representation-matching protocol is capable of reducing the communication or memory cost to almost the minimum in various tasks, including remote execution of unitary gate arrays, permutation gates, and unitary conjugation, as well as the storage and retrieval of unitary gates.

关键词： Quantum computation Quantum information processing

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Survey on Multi-Objective Evolutionary Algorithms 5

Survey on Multi-Objective Evolutionary Algorithms

引用

5th Annual International Conference on Network and information Systems for computers, ICNISC 2019

作者： Huang, Wenlan Zhang, Yu Li, Lan School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin150028 China

Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. This paper surveys the development of MOEA and its research status, classifies it into four categories, analyzes the advantages and disadvantages of these algorithms, and summarizes the main application fields of MOEA. Finally several viewpoints for the future research of MOEA are presented. © Published under licence by IOP Publishing Ltd.

关键词： Evolutionary algorithms

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Research on Wave-Off Risk Estimation Based on WODCP 3

Research on Wave-Off Risk Estimation Based on WODCP

引用

3rd IEEE Advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019

作者： Li, Hui Su, Xiaodong Zhang, Yuru Zhao, Ming Li, Yun Hao, Gang Harbin University of Commerce School of Computer and Information Engineering China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing China Heilongjiang University School of Electronic Engineering Harbin China

ISBN: (纸本)9781728105130

Consider the high risk of carrier-based aircraft wave-off operation, this paper has a research on wave-off decision making technology based on Wave-Off Decision Critical Position (WODCP). In this paper, after analyzing wave-off operation force, the wave-off decision making critical position should be defined. There are four steps of wave-off decision making technological process, including flight risk judge, wave-off risk judge, wave-off warning judge and wave-off decision making. Multiple examples simulations with and without LSO wave-off command based on flight path, wave-off surplus distance and wave-off risk have been compared to explain the important of wave-off decision making critical position. © 2019 IEEE.

关键词： Risk perception

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Self-Supervised Speech Denoising Using Only Noisy Audio Signals

arXiv

引用

arXiv 2021年

作者： Wu, Jiasong Li, Qingchun Yang, Guanyu Li, Lei Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China RennesF-35042 France The 28th Research Institute of China Electronics Technology Group Corporation Nanjing210007 China Univ Rennes INSERM LTSI UMR 1099 RennesF-35042 France

In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://***/liqingchunnnn/Only-Noisy-Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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MR-APG: An improved model for swarm intelligence movement coordination 2nd

MR-APG: An improved model for swarm intelligence movement co...

引用

2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Yang, Hui Ci, Linlin Zhang, Fuquan Yang, Minghua Mao, Yu Niu, Ke School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China RocketMilitary Equipment Research Institute Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9783030045845

Swarm intelligence based on the bionics have become an emerging research field, and researchers have proposed some kinds of intelligent models, such as ant colony, free group wolves, fish flock, insects group, bacteria and bacteria immune etc. The behavior of common features is: the individual behavior and function is simple, but a rally for the swarm after a complex cluster behavior ability. In mobile swarm group individuals within a certain space can keep a certain distance to avoid collision in order to group coordinated movement. In this paper, an attempt has been made to optimize the continuous and collaborative of the swarm, when the swarm nodes in the process of moving and turning. This paper puts forward the Multi-Restrained Artificial Pigeon Group (MR-APG) model based on multiple constraint factors, and introduce the concepts of quantitative coordination of nodes to optimize the nodes "over-dispersion" or "over-assembly" phenomenon. The experimental results show that, the higer α1 value will lead to average movement synchronization ratio μsync of MR-APG model will be lower. The results also show that, comparing with the traditional A/R model (μsync = 73%) and 3D-VFA model (μsync = 84%), the proposed MR-APG method can make μsync equal to 95%, moreover, its only takes half time of A/R model to make μsync equal to 80%. © Springer Nature Switzerland AG 2019.

关键词： Swarm intelligence

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